{"id":"W3183005013","doi":"10.1109/access.2021.3096139","title":"An Optimised Multivariable Regression Model for Predictive Analysis of Diabetic Disease Progression","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Multivariable calculus; Computer science; Regression analysis; Mean squared error; Field (mathematics); Health care; Data mining; Regression; Predictive modelling; Linear regression; Time series; Artificial intelligence; Machine learning; Statistics; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006472874,0.0002221473,0.0006039065,0.0002986502,0.0007494868,0.00002825892,0.000542209,0.0002777648,0.0003154483],"category_scores_gemma":[0.0009355328,0.0001840337,0.0002119824,0.00121671,0.00009460285,0.0005927621,0.0001455601,0.0003707015,0.000007313501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001741182,"about_ca_system_score_gemma":0.001128011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002723366,"about_ca_topic_score_gemma":0.0003429133,"domain_scores_codex":[0.9967018,0.0006293301,0.0009462472,0.0006689964,0.0004346292,0.0006189873],"domain_scores_gemma":[0.9954839,0.0009850266,0.0005661448,0.0009370715,0.001538909,0.0004889421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001680474,0.0008677546,0.4351161,0.001381371,0.0004152903,0.00001628292,0.006039986,0.5389059,0.007498571,0.0004772007,0.001386056,0.006215112],"study_design_scores_gemma":[0.0002939734,0.00006589132,0.008832522,0.0006423641,0.0006756485,4.29174e-8,0.0009020329,0.9743835,0.01123646,0.002751793,0.0000331395,0.0001826576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7651228,0.0002601274,0.2301888,0.0004822662,0.0007754773,0.00208466,0.0006673875,0.0001408431,0.0002776062],"genre_scores_gemma":[0.9914406,0.00005428129,0.006087259,0.0003330887,0.0001946982,0.001118015,0.0003304659,0.000045845,0.0003957505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4354776,"threshold_uncertainty_score":0.7504675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2205940020187808,"score_gpt":0.5561678301823652,"score_spread":0.3355738281635843,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}